from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		)

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=3 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=7 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__=99 ,  lowerCAmelCase__=36 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=37 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=5_12 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=6 ,  lowerCAmelCase__=6 ,  lowerCAmelCase__=3 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=None ,  lowerCAmelCase__=10_00 ,  )	->   str:

							'''simple docstring'''


							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				num_channels
							__lowercase					     =				image_size
							__lowercase					     =				patch_size
							__lowercase					     =				is_training
							__lowercase					     =				use_input_mask
							__lowercase					     =				use_token_type_ids
							__lowercase					     =				use_labels
							__lowercase					     =				vocab_size
							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				type_vocab_size
							__lowercase					     =				type_sequence_label_size
							__lowercase					     =				initializer_range
							__lowercase					     =				coordinate_size
							__lowercase					     =				shape_size
							__lowercase					     =				num_labels
							__lowercase					     =				num_choices
							__lowercase					     =				scope
							__lowercase					     =				range_bbox

							# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
							__lowercase					     =				text_seq_length
							__lowercase					     =				(image_size // patch_size) ** 2 + 1
							__lowercase					     =				self.text_seq_length + self.image_seq_length





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				ids_tensor([self.batch_size, self.text_seq_length] ,  self.vocab_size		)

							__lowercase					     =				ids_tensor([self.batch_size, self.text_seq_length, 4] ,  self.range_bbox		)
							__lowercase					     =				bbox.numpy()
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]		):
											for j in range(bbox.shape[1]		):
															if bbox[i, j, 3] < bbox[i, j, 1]:
																			__lowercase					     =				bbox[i, j, 3]
																			__lowercase					     =				bbox[i, j, 1]
																			__lowercase					     =				tmp_coordinate
															if bbox[i, j, 2] < bbox[i, j, 0]:
																			__lowercase					     =				bbox[i, j, 2]
																			__lowercase					     =				bbox[i, j, 0]
																			__lowercase					     =				tmp_coordinate
							__lowercase					     =				tf.constant(lowerCAmelCase__		)

							__lowercase					     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							__lowercase					     =				None
							if self.use_input_mask:
											__lowercase					     =				random_attention_mask([self.batch_size, self.text_seq_length]		)

							__lowercase					     =				None
							if self.use_token_type_ids:
											__lowercase					     =				ids_tensor([self.batch_size, self.text_seq_length] ,  self.type_vocab_size		)

							__lowercase					     =				None
							__lowercase					     =				None
							if self.use_labels:
											__lowercase					     =				ids_tensor([self.batch_size] ,  self.type_sequence_label_size		)
											__lowercase					     =				ids_tensor([self.batch_size, self.text_seq_length] ,  self.num_labels		)

							__lowercase					     =				LayoutLMvaConfig(
							    vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  initializer_range=self.initializer_range ,  coordinate_size=self.coordinate_size ,  shape_size=self.shape_size ,  input_size=self.image_size ,  patch_size=self.patch_size ,  )

							return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				TFLayoutLMvaModel(config=lowerCAmelCase__		)

							# text + image
							__lowercase					     =				model(lowerCAmelCase__ ,  pixel_values=lowerCAmelCase__ ,  training=lowerCAmelCase__		)
							__lowercase					     =				model(
							    lowerCAmelCase__ ,  bbox=lowerCAmelCase__ ,  pixel_values=lowerCAmelCase__ ,  attention_mask=lowerCAmelCase__ ,  token_type_ids=lowerCAmelCase__ ,  training=lowerCAmelCase__ ,  )
							__lowercase					     =				model(lowerCAmelCase__ ,  bbox=lowerCAmelCase__ ,  pixel_values=lowerCAmelCase__ ,  training=lowerCAmelCase__		)

							self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)		)

							# text only
							__lowercase					     =				model(lowerCAmelCase__ ,  training=lowerCAmelCase__		)
							self.parent.assertEqual(
							    result.last_hidden_state.shape ,  (self.batch_size, self.text_seq_length, self.hidden_size)		)

							# image only
							__lowercase					     =				model({'''pixel_values''': pixel_values} ,  training=lowerCAmelCase__		)
							self.parent.assertEqual(
							    result.last_hidden_state.shape ,  (self.batch_size, self.image_seq_length, self.hidden_size)		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				self.num_labels
							__lowercase					     =				TFLayoutLMvaForSequenceClassification(config=lowerCAmelCase__		)
							__lowercase					     =				model(
							    lowerCAmelCase__ ,  bbox=lowerCAmelCase__ ,  pixel_values=lowerCAmelCase__ ,  attention_mask=lowerCAmelCase__ ,  token_type_ids=lowerCAmelCase__ ,  labels=lowerCAmelCase__ ,  training=lowerCAmelCase__ ,  )
							self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				self.num_labels
							__lowercase					     =				TFLayoutLMvaForTokenClassification(config=lowerCAmelCase__		)
							__lowercase					     =				model(
							    lowerCAmelCase__ ,  bbox=lowerCAmelCase__ ,  pixel_values=lowerCAmelCase__ ,  attention_mask=lowerCAmelCase__ ,  token_type_ids=lowerCAmelCase__ ,  labels=lowerCAmelCase__ ,  training=lowerCAmelCase__ ,  )
							self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.text_seq_length, self.num_labels)		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				2
							__lowercase					     =				TFLayoutLMvaForQuestionAnswering(config=lowerCAmelCase__		)
							__lowercase					     =				model(
							    lowerCAmelCase__ ,  bbox=lowerCAmelCase__ ,  pixel_values=lowerCAmelCase__ ,  attention_mask=lowerCAmelCase__ ,  token_type_ids=lowerCAmelCase__ ,  start_positions=lowerCAmelCase__ ,  end_positions=lowerCAmelCase__ ,  training=lowerCAmelCase__ ,  )
							self.parent.assertEqual(result.start_logits.shape ,  (self.batch_size, self.seq_length)		)
							self.parent.assertEqual(result.end_logits.shape ,  (self.batch_size, self.seq_length)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.prepare_config_and_inputs()
							((__lowercase)     ,	(__lowercase)     ,	(__lowercase)     ,	(__lowercase)     ,	(__lowercase)     ,	(__lowercase)     ,	(__lowercase)     ,	(__lowercase))					     =				config_and_inputs
							__lowercase					     =				{
							    '''input_ids''': input_ids,
							    '''bbox''': bbox,
							    '''pixel_values''': pixel_values,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict




@require_tf
class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Optional[int]     					=		(
			    (
			        TFLayoutLMvaModel,
			        TFLayoutLMvaForQuestionAnswering,
			        TFLayoutLMvaForSequenceClassification,
			        TFLayoutLMvaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			__a    :					List[str]     					=		(
			    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
			    if is_tf_available()
			    else {}
			)

			__a    :					List[Any]     					=		False
			__a    :					List[str]     					=		False
			__a    :					str     					=		False





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							return True





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__=False		)	->   dict:

							'''simple docstring'''


							__lowercase					     =				copy.deepcopy(lowerCAmelCase__		)

							if model_class in get_values(lowerCAmelCase__		):
											__lowercase					     =				{
											    k: tf.tile(tf.expand_dims(lowerCAmelCase__ ,  1		) ,  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
											    if isinstance(lowerCAmelCase__ ,  tf.Tensor		) and v.ndim > 0
											    else v
											    for k, v in inputs_dict.items()
											}

							if return_labels:
											if model_class in get_values(lowerCAmelCase__		):
															__lowercase					     =				tf.ones(self.model_tester.batch_size ,  dtype=tf.intaa		)
											elif model_class in get_values(lowerCAmelCase__		):
															__lowercase					     =				tf.zeros(self.model_tester.batch_size ,  dtype=tf.intaa		)
															__lowercase					     =				tf.zeros(self.model_tester.batch_size ,  dtype=tf.intaa		)
											elif model_class in get_values(lowerCAmelCase__		):
															__lowercase					     =				tf.zeros(self.model_tester.batch_size ,  dtype=tf.intaa		)
											elif model_class in get_values(lowerCAmelCase__		):
															__lowercase					     =				tf.zeros(
															    (self.model_tester.batch_size, self.model_tester.text_seq_length) ,  dtype=tf.intaa		)

							return inputs_dict





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				TFLayoutLMvaModelTester(self		)
							__lowercase					     =				ConfigTester(self ,  config_class=lowerCAmelCase__ ,  hidden_size=37		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							self.config_tester.run_common_tests()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
											__lowercase					     =				model_class(lowerCAmelCase__		)
											if getattr(lowerCAmelCase__ ,  '''hf_compute_loss''' ,  lowerCAmelCase__		):
															# The number of elements in the loss should be the same as the number of elements in the label
															__lowercase					     =				self._prepare_for_class(inputs_dict.copy() ,  lowerCAmelCase__ ,  return_labels=lowerCAmelCase__		)
															__lowercase					     =				prepared_for_class[
															    sorted(prepared_for_class.keys() - inputs_dict.keys() ,  reverse=lowerCAmelCase__		)[0]
															]
															__lowercase					     =				added_label.shape.as_list()[:1]

															# Test that model correctly compute the loss with kwargs
															__lowercase					     =				self._prepare_for_class(inputs_dict.copy() ,  lowerCAmelCase__ ,  return_labels=lowerCAmelCase__		)
															__lowercase					     =				prepared_for_class.pop('''input_ids'''		)

															__lowercase					     =				model(lowerCAmelCase__ ,  **lowerCAmelCase__		)[0]
															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

															# Test that model correctly compute the loss when we mask some positions
															__lowercase					     =				self._prepare_for_class(inputs_dict.copy() ,  lowerCAmelCase__ ,  return_labels=lowerCAmelCase__		)
															__lowercase					     =				prepared_for_class.pop('''input_ids'''		)
															if "labels" in prepared_for_class:
																			__lowercase					     =				prepared_for_class['''labels'''].numpy()
																			if len(labels.shape		) > 1 and labels.shape[1] != 1:
																							__lowercase					     =				-1_00
																							__lowercase					     =				tf.convert_to_tensor(lowerCAmelCase__		)
																							__lowercase					     =				model(lowerCAmelCase__ ,  **lowerCAmelCase__		)[0]
																							self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
																							self.assertTrue(not np.any(np.isnan(loss.numpy()		)		)		)

                # Test that model correctly compute the loss with a dict
															__lowercase					     =				self._prepare_for_class(inputs_dict.copy() ,  lowerCAmelCase__ ,  return_labels=lowerCAmelCase__		)
															__lowercase					     =				model(lowerCAmelCase__		)[0]
															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

															# Test that model correctly compute the loss with a tuple
															__lowercase					     =				self._prepare_for_class(inputs_dict.copy() ,  lowerCAmelCase__ ,  return_labels=lowerCAmelCase__		)

															# Get keys that were added with the _prepare_for_class function
															__lowercase					     =				prepared_for_class.keys() - inputs_dict.keys()
															__lowercase					     =				inspect.signature(model.call		).parameters
															__lowercase					     =				list(signature.keys()		)

															# Create a dictionary holding the location of the tensors in the tuple
															__lowercase					     =				{0: '''input_ids'''}
															for label_key in label_keys:
																			__lowercase					     =				signature_names.index(lowerCAmelCase__		)
																			__lowercase					     =				label_key
															__lowercase					     =				sorted(tuple_index_mapping.items()		)
															# Initialize a list with their default values, update the values and convert to a tuple
															__lowercase					     =				[]

															for name in signature_names:
																			if name != "kwargs":
																							list_input.append(signature[name].default		)

															for index, value in sorted_tuple_index_mapping:
																			__lowercase					     =				prepared_for_class[value]

															__lowercase					     =				tuple(lowerCAmelCase__		)

															# Send to model
															__lowercase					     =				model(tuple_input[:-1]		)[0]

															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							(
							    (
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	
							)					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							(
							    (
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	
							)					     =				self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											__lowercase					     =				type
											self.model_tester.create_and_check_model(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							(
							    (
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	
							)					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							(
							    (
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	
							)					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							(
							    (
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	(
							    __lowercase
							)     ,	
							)					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)





			@slow
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase					     =				TFLayoutLMvaModel.from_pretrained(lowerCAmelCase__		)
											self.assertIsNotNone(lowerCAmelCase__		)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image




@require_tf
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			@cached_property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__		) if is_vision_available() else None





			@slow
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''		)

							__lowercase					     =				self.default_image_processor
							__lowercase					     =				prepare_img()
							__lowercase					     =				image_processor(images=lowerCAmelCase__ ,  return_tensors='''tf'''		).pixel_values

							__lowercase					     =				tf.constant([[1, 2]]		)
							__lowercase					     =				tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]		) ,  axis=0		)

							# forward pass
							__lowercase					     =				model(input_ids=lowerCAmelCase__ ,  bbox=lowerCAmelCase__ ,  pixel_values=lowerCAmelCase__ ,  training=lowerCAmelCase__		)

							# verify the logits
							__lowercase					     =				(1, 1_99, 7_68)
							self.assertEqual(outputs.last_hidden_state.shape ,  lowerCAmelCase__		)

							__lowercase					     =				tf.constant(
							    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,  lowerCAmelCase__ ,  atol=1E-4		)		)
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()